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The cellular design and hardware implementation of these mechanisms constitute the core of this paper.
There is still room, however, for improvements in the implementation of these mechanisms and in the adoption of the internationally accepted CG best practices.
We present the design rationale and describe our implementation of these mechanisms and discuss their advantages in meeting the needs of several popular types of applications.
The underlying neural implementation of these mechanisms is receiving growing attention because of its implications for professional, social, and medical contexts.
We present results from experiments using our implementation of these mechanisms at the middleware that comprises from node firmware to the PC driver.
Yet the implementation of these mechanisms is as rare as the operational research on this topic.
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We stress that some implementation details of these mechanisms will depend on the details of the application, and so are left undefined here.
HCI defines the implementation of the mechanisms that establish the efficient communication protocols between human and machines.
For detecting re-establishments, ELFN uses a probing mechanism and the implementation of this mechanism is not easy.
Secondly, it establishes the general architecture, procedures and restrictions for a distributed implementation of the mechanism in DenseNets.
This article examines the implementation of this mechanism and obtains the optimal thresholds that maximize operational performance.
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